Abstract
Objective: This paper aimed to improve the accuracy of identification of small individual betel nuts and the degree of automation of betel nut processing plant by combining with deep learning. Methods: In this study, a novel feature extraction network named Mob-darknet-52 was proposed to construct a method of betel nut location and recognition based on improved YOLO algorithm by using multi-scale detection size. Results: the test showed that the proposed method had a detection accuracy of 94.8%, an accuracy rate of 94.5%, a recall rate of 95.1%, and a detection time of 6.679 ms in betel nut classification. Conclusion: The optimized algorithm based on improved YOLOV3 network can realize the rapid location and identification of betel nut in dense environment.
Publication Date
6-5-2023
First Page
83
Last Page
88
DOI
10.13652/j.spjx.1003.5788.2022.80816
Recommended Citation
Yun, DAI; Ming, LU; Ting, HE; and Cheng-wu, PENG
(2023)
"Fast recognition method for betel nut in dense environments based on improved YOLO,"
Food and Machinery: Vol. 39:
Iss.
4, Article 15.
DOI: 10.13652/j.spjx.1003.5788.2022.80816
Available at:
https://www.ifoodmm.cn/journal/vol39/iss4/15
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